Metadata-Version: 2.1
Name: autodistill-vlpart
Version: 0.1.0
Summary: VLPart for use with Autodistill
Home-page: https://github.com/autodistill/autodistill-vlpart
Author: Roboflow
Author-email: support@roboflow.vom
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: autodistill
Requires-Dist: supervision
Requires-Dist: torch
Requires-Dist: numpy
Provides-Extra: dev
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: black ==22.3.0 ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: twine ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: wheel ; extra == 'dev'

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill VLPart Module

This repository contains the code supporting the VLPart base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[VLPart](https://github.com/facebookresearch/VLPart), developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary. `autodistill-vlpart` enables you to use VLPart to auto-label images for use in training a fine-tuned model. `autodistill-vlpart` supports the LVIS vocabulary.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [VLPart Autodistill documentation](https://autodistill.github.io/autodistill/base_models/vlpart/).

## Installation

To use VLPart with autodistill, you need to install the following dependency:

```bash
pip3 install autodistill-vlpart
```

## Quickstart

```python
from autodistill_vlpart import VLPart
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our VLPart prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = VLPart(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

predictions = base_model.predict("./image.png")

print(predictions)

# label the images in the context_images folder
base_model.label("./context_images", extension=".jpeg")
```

## License

This project is licensed under an [MIT license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
